Chen Bernard, Pellicer Stephen, Tai Phang C, Harrison Robert, Pan Yi
Department of Computer Science, University of Central Arkansas, 201 Donaghey Ave. MCST304, Conway, AR 72035, USA.
Int J Comput Biol Drug Des. 2009;2(2):168-86. doi: 10.1504/IJCBDD.2009.028822. Epub 2009 Oct 3.
Protein sequence motifs have the potential to determine the conformation, function and activities of the proteins. In order to obtain protein sequence motifs which are universally conserved across protein family boundaries, unlike most popular motif discovering algorithms, our input dataset is extremely large. As a result, an efficient technique is demanded. We create two granular computing models to efficiently generate protein motif information which transcend protein family boundaries. We have performed a comprehensive comparison between the two models. In addition, we further combine the results from the FIK and FGK models to generate our best sequence motif information.
蛋白质序列基序有可能决定蛋白质的构象、功能和活性。为了获得跨越蛋白质家族边界普遍保守的蛋白质序列基序，与大多数流行的基序发现算法不同，我们的输入数据集非常大。因此，需要一种高效的技术。我们创建了两个粒度计算模型，以有效地生成超越蛋白质家族边界的蛋白质基序信息。我们对这两个模型进行了全面比较。此外，我们进一步结合FIK和FGK模型的结果，以生成我们最佳的序列基序信息。
